Syndromic surveillance as a predictive tool for health-related school absences in COVID-19 Sentinel Schools in Catalonia, Spain. (preprint)

Monitoring influenza-like illness through syndromic surveillance could be an important strategy in the COVID-19 emergence scenario. The study aims to implement syndromic surveillance for children aged 6-11 years in COVID-19 sentinel schools in Catalonia. Data collection was made by self-applied survey to collect daily health status and symptoms. We proceed logistic mixed models and a Latent Class Analysis to investigate associations with syndromes and school absence. Were enrolled 135 students (2163 person-days) that filled 1536 surveys and 60 participants reported illness (29.52 by 100 person/day) and registered 189 absence events, 62 of them (32.8%) related to health reasons. Subgroups of influenza-like illness were founded such as a significantly and positively association with school absences. The findings of this study can be applied to the detection of health events, and association with school absences, offering an opportunity for quick action, or simply for monitoring and understanding the students' health situation.

Monitoring Social Distancing and SARS-CoV-2 Transmission in Brazil Using Cell Phone Mobility Data (preprint)

BACKGROUND: In the absence of vaccines and effective pharmacological interventions to reduce with the COVID-19 transmission, social distancing measures have been implemented to mitigate the impact on healthcare systems and secure time to prepare the public health response. METHODS: We assessed the relationship between mobility data collected by mobile phone and time-dependent reproduction number R(t) using severe acute respiratory illness cases reported by 102 Brazilian cities with COVID-19 confirmed cases until April 15, grouped by demographic density (low, intermediate and high). FINDINGS: The mean social distancing index from February 1 to April 15 was 43.6% (27.2% to 63.7%), and no significant difference observed comparing the groups of demographic density (p-value = 0.809). The social distancing index measure obtained from mobility data was able to predict future values of R(t) in all groups of demographic density. Furthermore, using SARI cases, cross-correlation analyses showed that isolation was highly correlated with R(t) (ccfINTERPRETATION: The early implementation of social distancing measures greatly reduced the COVID-19 spread. A major advantage to our approach is that the social distancing index data is available on a daily basis, in contrast with R(t) measurement, which is subject to significant delays. This index metric can be monitored in real time to assess adherence to social distancing measures and help guide, with real time data, the public health policy decision making process.FUNDING: FADQ and JC were granted a fellowship for research productivity from the Brazilian National Council for Scientific and Technological Development – CNPq, process/contract identification: 312656/2019-0 and 310551/2018-8, respectively.DECLARATION OF INTERESTS: Authors declare no competing interests.ETHICS APPROVAL STATEMENT: This study followed Brazilian and International legislation for conducting human research. This research project was approved by the National Research Ethics Committee (Comissão Nacional de Ética em Pesquisa, CONEP) in Brazil, Register number (CAAE): 11946619.5.0000.5421.

Monitoring social distancing and SARS-CoV-2 transmission in Brazil using cell phone mobility data (preprint)

Social distancing measures have emerged as the predominant intervention for containing the spread of COVID-19, but evaluating adherence and effectiveness remains a challenge. We assessed the relationship between aggregated mobility data collected from mobile phone users and the time-dependent reproduction number R(t), using severe acute respiratory illness (SARI) cases reported by Sao Paulo and Rio de Janeiro. We found that the proportion of individuals staying home all day (isolation index) had a strong inverse correlation with R(t) (rho

Epidemiological and clinical characteristics of the early phase of the COVID-19 epidemic in Brazil (preprint)

BackgroundThe first case of COVID-19 was detected in Brazil on February 25, 2020. We report the epidemiological, demographic, and clinical findings for confirmed COVID-19 cases during the first month of the epidemic in Brazil. MethodsIndividual-level and aggregated COVID-19 data were analysed to investigate demographic profiles, socioeconomic drivers and age-sex structure of COVID-19 tested cases. Basic reproduction numbers (R0) were investigated for Sao Paulo and Rio de Janeiro. Multivariate logistic regression analyses were used to identify symptoms associated with confirmed cases and risk factors associated with hospitalization. Laboratory diagnosis for eight respiratory viruses were obtained for 2,429 cases. FindingsBy March 25, 1,468 confirmed cases were notified in Brazil, of whom 10% (147 of 1,468) were hospitalised. Of the cases acquired locally (77{middle dot}8%), two thirds (66{middle dot}9% of 5,746) were confirmed in private laboratories. Overall, positive association between higher per capita income and COVID-19 diagnosis was identified. The median age of detected cases was 39 years (IQR 30-53). The median R0 was 2{middle dot}9 for Sao Paulo and Rio de Janeiro. Cardiovascular disease/hypertension were associated with hospitalization. Co-circulation of six respiratory viruses, including influenza A and B and human rhinovirus was detected in low levels. InterpretationSocioeconomic disparity determines access to SARS-CoV-2 testing in Brazil. The lower median age of infection and hospitalization compared to other countries is expected due to a younger population structure. Enhanced surveillance of respiratory pathogens across socioeconomic statuses is essential to better understand and halt SARS-CoV-2 transmission. FundingSao Paulo Research Foundation, Medical Research Council, Wellcome Trust and Royal Society.

The impact of early social distancing at COVID-19 Outbreak in the largest Metropolitan Area of Brazil. (preprint)

We evaluated the impact of early social distancing on the COVID-19 transmission in the Sao Paulo metropolitan area. Using an age-stratified SEIR model, we determined the time-dependent reproductive number, and forecasted the ICU beds necessary to tackle this epidemic. Within 60 days, these measures might prevent 89,133 deaths.

A model to predict SARS-CoV-2 infection based on the first three-month surveillance data in Brazil. (preprint)

Background: COVID-19 diagnosis is a critical problem, mainly due to the lack or delay in the test results. We aimed to obtain a model to predict SARS-CoV-2 infection in suspected patients reported to the Brazilian surveillance system. Methods: We analyzed suspected patients reported to the National Surveillance System that corresponded to the following case definition: patients with respiratory symptoms and fever, who traveled to regions with local or community transmission or who had close contact with a suspected or confirmed case. Based on variables routinely collected, we obtained a multiple model using logistic regression. The area under the receiver operating characteristic curve (AUC) and accuracy indicators were used for validation. Results: We described 1468 COVID-19 cases (confirmed by RT-PCR) and 4271 patients with other illnesses. With a data subset, including 80% of patients from Sao Paulo (SP) and Rio Janeiro (RJ), we obtained a function which reached an AUC of 95.54% (95% CI: 94.41% - 96.67%) for the diagnosis of COVID-19 and accuracy of 90.1% (sensitivity 87.62% and specificity 92.02%). In a validation dataset including the other 20% of patients from SP and RJ, this model exhibited an AUC of 95.01% (92.51% - 97.5%) and accuracy of 89.47% (sensitivity 87.32% and specificity 91.36%). Conclusion: We obtained a model suitable for the clinical diagnosis of COVID-19 based on routinely collected surveillance data. Applications of this tool include early identification for specific treatment and isolation, rational use of laboratory tests, and input for modeling epidemiological trends.